By tracing the limits of introspection and behaviorism and highlighting advances in linguistics, computer science, and neuroscience, the history provides the context and pressures that propelled the shift toward cognitive approaches.
The Cognitive Revolution was a mid-20th-century shift in psychology that refocused the field on the scientific study of mental processes such as attention, memory, language, and problem solving. It emerged in response to the limits of strict behaviorism and was propelled by developments in linguistics, computer science, information theory, and neuroscience. This movement introduced the mind-as-information-processor metaphor, emphasized internal representations, and advanced rigorous experimental and computational methods. Its legacy underpins modern cognitive psychology and cognitive neuroscience, with wide applications in education, clinical practice, AI, and human-computer interaction.
What Was the Cognitive Revolution?
The Cognitive Revolution was a paradigm shift in psychology that began in the 1950s and 1960s, moving the discipline beyond behaviorism’s exclusive focus on observable behavior to include the scientific study of internal mental processes. It framed the mind as an information-processing system that encodes, stores, transforms, and retrieves representations of the world.
Why It Emerged
Limits of behaviorism: Explaining language, reasoning, and complex learning proved difficult without reference to internal processes.
Influence of computer science and information theory: Concepts like input, output, memory, and processing provided powerful metaphors and models.
Linguistics: Chomsky’s critique of behaviorist accounts of language highlighted innate structures and rules.
Wartime research and engineering: Human factors and signal detection work demanded models of attention and decision-making.
Gestalt insights: Emphasis on patterns and organizational principles prefigured cognitive approaches to perception.
Core Assumptions and Concepts
Information processing: The mind processes information through stages or systems (e.g., sensory registers, short-term/working memory, long-term memory).
Mental representations: Knowledge is represented internally (e.g., schemas, propositions, images) and can be manipulated.
Limited capacity: Attention and working memory have constraints, shaping performance and errors.
Serial and parallel operations: Some processes occur step-by-step, others simultaneously.
Top-down and bottom-up processing: Perception and thought reflect both incoming data and prior knowledge/expectations.
Levels of analysis: Cognitive phenomena can be understood at computational (what/why), algorithmic (how), and implementational (physical) levels.
Methods and Tools
Experimental paradigms: Reaction-time (mental chronometry), accuracy, and signal detection measures to infer hidden processes.
Memory tasks: Span tests, serial position curves, priming, and working-memory dual-task methods.
Attention and perception: Dichotic listening, visual search, Stroop, and change detection.
Cognitive modeling: Symbolic models, production systems, Bayesian models, and connectionist networks.
Neurocognitive approaches: Cognitive neuropsychology (case studies of brain lesions), neuroimaging (fMRI, EEG/ERP), and eye tracking.
Open science and replication: Pre-registration, data sharing, and cumulative modeling to strengthen reliability.
Key Figures and Milestones
George A. Miller (1956): "The Magical Number Seven, Plus or Minus Two" highlighted capacity limits in immediate memory.
Donald Broadbent (1958): Early filter model of attention integrated information-processing ideas.
Noam Chomsky (1959): Critique of behaviorist language theory underscored internal grammatical structures.
Ulric Neisser (1967): Textbook "Cognitive Psychology" crystallized the movement.
Atkinson & Shiffrin (1968): Multi-store memory model; later refined by Baddeley & Hitch’s working memory (1974).
Newell & Simon: Problem solving and the development of symbolic AI and production systems.
Clinical practice: Cognitive and cognitive-behavioral therapies target maladaptive thoughts and biases.
Human-computer interaction: Interface design informed by attention, memory, and decision principles.
Artificial intelligence: Mutual exchange of models and methods between AI and cognitive science.
Law and policy: Eyewitness memory, decision biases, and risk communication.
Critiques and Extensions
Ecological validity: Calls for studying cognition in real-world contexts.
Embodied, embedded, and enactive views: Cognition depends on the body, environment, and action.
Connectionism and dynamical systems: Emphasize distributed representations and continuous change.
Affect and motivation: Integration of emotion and goals with cognition (hot cognition).
Cultural and social cognition: How culture, language, and social interaction shape thought.
Where It Stands Today
The Cognitive Revolution established a durable framework for investigating the mind using experiments, formal models, and neural measures. Today, cognitive psychology and cognitive neuroscience integrate with data science and machine learning, addressing complex phenomena—from language and memory to decision-making and creativity—while striving for ecological validity, reproducibility, and cross-disciplinary synthesis.
In the Atkinson and Shiffrin (1968) multi-store model, information passes from sensory registers into _____ before potential encoding in long-term memory.
Baddeley and Hitch’s 1974 proposal reframed short-term memory as a multi-component working memory system.
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Cognitive Psychology
The Cognitive Revolution led to the establishment and expansion of cognitive psychology as a field centered on internal mental processes and information processing.
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Memory
Reoriented psychology toward internal processes, catalyzing rigorous study of memory (encoding, storage, retrieval) via information-processing models, laboratory paradigms, and computational approaches.
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Cognitive Psychology
By reinstating the scientific study of internal mental processes and introducing information-processing frameworks, the Cognitive Revolution directly produced the modern field of cognitive psychology.
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Information Processing Model
The cognitive revolution established the mind-as-information-processor metaphor, leading to formal stage-based and computational models explaining attention, memory, language, and problem solving.
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Computational Modeling
The shift to viewing the mind as an information processor spurred formal, testable computational models of cognition, from symbolic architectures to connectionist and Bayesian approaches.
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Cognitive Load Theory
By reframing the mind as an information-processing system with limited working-memory capacity, the cognitive revolution provided the theoretical basis for cognitive load theory’s instructional design principles.
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Human Factors
By introducing information-processing models and emphasizing attention, memory limits, and decision-making, the cognitive revolution shaped human factors methods for user-centered design, workload assessment, error analysis, and HCI.